Enhancement in Grid Performance Using Coordinated V2G Approach with Residential EV Charging Demand

EasyChair Preprint 15797

Abstract

The recent advancement in battery technologies is promoting the adoption of electric vehicles (EVs) worldwide. The large-scale penetration of EVs will impose significant EV charging demand on the residential area of the distribution network. This significant EV charging demand at home may coincide with the peak demand of the distribution system, resulting in an enhanced system's peak demand. Which may create a bottleneck in supplying capacity and necessitate time-consuming grid upgradation. This paper aims to reduce the peak demand for distribution systems using charging/discharging scheduling of the EVs in the residential area. The strategy proposes an EV energy management system (EVMS) that coordinates EVs for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes both at residential parking zone (RPZ). The results are compared with the uncoordinated charging that includes the V2G mode only and signify that the proposed EVMS can reduce the system peak demand by 38.63\%, peak power losses by 57.47\% and improve node voltage profiles with 6.04\% while providing 88.93\% benefits to the customers with by shifting their charging demand in less pricing hours and participating in V2G mode. The results also indicate the proposed EVMS model not only eliminates the increased EV charging demand from the system peak but also reduces its already existing peak by 12.9\%. The simulation results are performed on the 33-bus standard test bench distribution system.

Keyphrases: Dynamic electricity pricing, EV energy management system, G2V, V2G, electric vehicles, residential parking zones

@booklet{EasyChair:15797,
  author    = {Shakti Vashisth and Seema Bairwa and Anil Swarnkar and Praveen Kumar Agrawal and Nikhil Gupta and K. R. Niazi},
  title     = {Enhancement in Grid Performance Using Coordinated V2G Approach with Residential EV Charging Demand},
  howpublished = {EasyChair Preprint 15797},
  year      = {EasyChair, 2025}}